Structured Data & the Future of Educational Material
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STRUCTURED DATA &THE FUTURE OF EDUCATIONAL MATERIAL
Paul Groth | @pgroth | pgroth.com
Disruptive Technology Director
Elsevier Labs | @elsevierlabs
September 28, 2016 - Onderwijs en Linked Data – wat waarom en hoe
Thanks to: Mike Lauruhn, Mary Millar , David Kuilman
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ELSEVIER LABS - INTRO
WORLD LEADER IN DIGITAL INFO SOLUTIONS2
Published over 330,000 articlesin 2013
Founded over 130 years ago
Work with over 30 million Scientists, students, health & information professionals
Employ over 7,000 employeesin 24 countries
Received over 1 million submissions in 2013
Over the last50 yearsthe majority of Noble Laureates have publishedwith Elsevier
Over 53 million items indexed by Scopus
Elsevier eBooks, OnlineJournals, Databases
Publishes over 2,200 online journals & over 10,000 e-books
SOLUTIONS
ElsevierR+D Solutions
ElsevierClinical Solutions
Helps corporate researchers, R+D professionals, and engineers improve how they interact with, share, and apply information to solve problems using our digital workflow tools, analytics, and data
Provides universities, governments, and research institutions with the resources and insights to improve institutional research strategy, management, and performance.
ElsevierEducation
Helps medical professionals apply trusted data and sophisticated tools to make better clinical decisions, deliver better care, and produce better healthcare outcomes.
Helps educate highly-skilled, effective healthcare professionals, using the most advanced pedagogical tools and reference works.
ElsevierResearch Intelligence
CONTENT
CA
PAB
ILIT
IES
PLAT
FOR
MS
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40 million reactions 75 million compounds500 million facts
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HOW DO WE ENABLE LEARNERS TO ACQUIRE AND APPLY ALL THIS KNOWLEDGE EFFECTIVELY?
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THREE PROJECTS
1. Nursing education
2. Linked data for learning about linked data
3. Using research resources to facilitate education
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http://www.theatlantic.com/health/archive/2016/02/nursing-shortage/459741/
According to the Bureau of Labor Statistics, 1.2 million vacancies will emerge for registered nurses between 2014 and 2022.
By 2025, the shortfall is expected to be “more than twice as large as any nurse shortage experienced since the introduction of Medicare and Medicaid in the mid-1960s,” a team of Vanderbilt University nursing researchers wrote…
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10
ELSEVIER’S SHERPATH
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Product Design (Student facing)
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PROBLEMS ADDRESSED
I have too much content to consume.I have limited time to study, so I
need to know where to concentrate.
1. PERSONALIZED LEARNING TUTOR Reminders and prioritization assistance to help students optimize available study time and balance test prep with assignment completion.
3. QUIZZING COACH Test prep featuring content recommended based on upcoming goals, time-available, and current areas of weakness.
2. ENGAGING LEARNING EXPERIENCE Media-rich learning instructionally-designed to convey essential information through bite-sized content, quizzes, activities, and feedback.
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WHAT IS A RECOMMENDER?
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For most businesses, recommenders are essential for surfacing items to consumers. For education, recommenders enable adaptive and enhanced learning.
Recommendation engines come in different types: content-based, knowledge-based, collaborative-filtering, and hybrids.
Content-based looks for items that users view or purchase together
Knowledge-based applies metadata to make inferences about the similarity of items
Collaborative-filtering looks for similarity among users to make item recommendations
Hybrid systems combine capabilities of different types and reconcile the recommendations from each
Recommenders are tools for interacting with large and complex information spaces. They provide a personalized and prioritized view of items in the space.
Rec Tech
Content + User Data
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Recommender System
MACHINE LEARNING RECOMMENDER COMPOSITION
OUR RECOMMENDER COMPOSITION
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Our Recommender System is composed of the recommendation technology (engines, analytics, search, etc.) and content & user data.
To make effective recommendations for our use cases, the system needs to be built for the content & user data in our domain.
In our own system, technology is built to match the content.Le
arni
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onte
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ite-S
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LContent & User Data
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ENGINESNLP, Analysis, Modeling
SEARCHSemantic, Filtering
ANALYTICSCollection, Processing
INTEGRATIONAPIs, Connections
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Discovery using Recommender algorithms
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Learning Object
Student Learning Objective
Student Learning Objective
Student Learning Objective
edu:teaches
edu:requires
Bloom concept
skos:narrowerMatch
skos:broader
Content Object
ecm:hasRecommendation
edu:remediates
Question Object
edu:assesses
edu:teaches
ecm:hasRecommendedAssessment
Student Learning Objectiveskos:narrower
QSEN concept
skos:broaderMatch
Recommender algorithms can help:- Identify test banks- Alternate content in books- Alternate content for learning
objective
Manual, expert curation can help:- Set questions to objectives- Set remediation content for a learning object- Set alternative content to learning objective
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QUIZ COACH EXAMPLE
QUIZ COACH WORKED EXAMPLE
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User Flow1. Student chooses to take 15 questions on their Weak Areas, covering Chain of Infection2. Quiz Coach service generates a quiz using the Recommender
Recommender fills SLO “slots” from a pool of responses (questions) for each SLO.Constraints:1. Time – Total time available (selected), time per
question2. Depth – Prioritizes breadth over depth3. Novelty – Unseen, Incorrect,
Skipped/Delivered/Unanswered, Correct4. Difficulty – increasing order or difficulty range5. Content preference – source of questions
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HOW SHERPATH USES CONTENT
16Product Content
Learning Content is purpose-built to fit our content model (individual learning objects that teach or assess learning objectives) and tagged to SLOs.
Our SLOs are a new unified taxonomy of domain learning outcomes that map didactic and clinical objectives.
Reference content is available as structured XML for each of our 53 core Nursing titles
EMMeT is used to text mine the XML for relevant health concepts to save manual tagging
In EOLS, new and legacy content are integrated on every page. Users consume content designed for digital learning, but can also request “More Details” straight from ELS book content when they’re confused.
RecAPI
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CONTENT MODEL COMPONENTS
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• Elsevier Enterprise Content Model ontology• 40+ properties• 20 datatypes• 10 Content types• 20 Asset types
• Adaptive Learning ontology• Recommendation• Teaching• Assessing• Remediation
• SKOS ontology • 3 third-party vocabularies: QSEN, Bloom etc.
• QTI 2.1 compliant schema• XHTML5 schema
• 50+ data-type attribute definitions
• Student Learning Objective ontology• SKOS ontology extended with 2 properties
• Multi-media assets incl. Text Time based Markup Language
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THE ROLE OF STRUCTURED DATA• Content needs be organized/tagged at a fine grained level
• Content organization helps drive recommendation
• Allow for the recommendation of specific pieces of content• Allow for the combination of multiple pieces of content to support a
student’s journey
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Learner Transit Map courtesy Stuart Sutton
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THE ROLE OF STRUCTURED DATA• Central aggregation enables the location of material
• Structured data eases aggregation especially if produced by multiple providers
• Structure enables a separation between defining the learning goals/objectives and the variety of content that can support that
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What’s the role of academic / research content in education?
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Challenge: how can we use these resources to improve education?
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3. Mai 2023LinkedUp – Michael Lauruhn 28
Syllabus Discover Application:• Prototype allows user to make connections between a
syllabus and a corpus of journal article content• Live demo (with LinkedUp Catalogue Water & Ecology
content) available online• Configurable code available on Github
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Clustering & Visualisation Discover Application:• Prototype provides user a visualisation of concepts from a corpus of journal
article content based on clustering• Live demo (with LinkedUp Catalogue Water & Ecology content) available
online• Configurable code available on Github
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THE ROLE OF STRUCTURED DATA
• Establishing structure enables new applications to be built
• Allows rethinking the role of existing content with respect to educational apps
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CONCLUSION• Education increasingly needs to be personalized, adaptive, on-demand
and current
• Technology is central
• But technology requires the right kinds of content (bite size, reusable, malleable)
• Linked data standards and methods are an important mechanism for delivery
Contact: Paul Groth @pgroth